'More Events, More Medals, More Interest!': The Growth and Expansion of the Winter Olympic Games
Bibliographic record
Abstract
The most recent Winter Olympics in Sochi witnessed an unprecedented addition of 12 new winter sports events. The result was a record 2800 athletes competing in 15 sports, comprising 98 events, and the awarding of 294 medals. At the first Winter Games in 1924, there were only 16 events and 49 medals awarded. By the time the 1988 Calgary Games rolled around, it had grown to 138 medals. This study traced the historical development in the growth of the Winter Games with comparisons made with the Summer Olympics. Primary and secondary written sources were examined and participant observation techniques were employed with the investigator attending a number Olympic Games. A review of the concept of “demonstration sports” was undertaken and how it has been replaced by the IOC evaluating new events with key factors being sport federation lobbying, television “friendliness”, the public appeal of events, political issues (e.g. voting by IOC members on the dropping and adding of new sports) and social factors (e.g. gender balance). It was found that the growth of the Winter Olympics program can be attributed to several key reasons: the new Olympic cycle when the IOC made its decision to put the Winter and Summer Games on off-setting schedules; the obvious television appeal of many of the new events; gender balance with more women’s events and now more mixed events getting the competitors closer to a state of equilibrium; and pressure from the X Games as the IOC tries to keep pace with the times and maintain a connection to the younger generation. With the Winter Games moving on to Pyeongchang for its 2018 edition, the study concludes with an analysis of what new developments are possibly in store.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".